Papers

Timely assessment of depression severity in adolescents is essential for guiding clinical care, yet conventional evaluations rely on episodic clinical assessments and symptom rating scales. Contactless gait analysis offers a potentially scalable approach to capturing objective behavioral features associated with depression. Here, we introduce the Long-Distance 3D Adolescent Depression Gait Dataset (LD3D-ADG), comprising depth-sensor gait recordings from 119 adolescents at a single center, including 39 healthy controls, 41 with mild depression, and 39 with moderate-to-severe depression. We developed UMLPA-GCN, a graph convolutional network that models spatiotemporal gait dynamics across three anatomically defined skeletal partitions. In participant-independent five-fold cross-validation, UMLPA-GCN achieved 75.63% classification accuracy, a macro-F1 score of 75.52%, and a macro-area under the receiver operating characteristic curve of 0.839 (95% confidence interval, 0.779–0.899). Class-specific recall was 87.18%, 63.41%, and 76.92% for healthy controls, mild depression, and moderate-to-severe depression, respectively. At the participant level, reduced arm swing, slower pelvic movement, restricted joint range of motion, and greater arm-swing asymmetry were identified as leading potential digital biomarkers contributing to depression severity assessment, associated with higher 17-item Hamilton Depression Rating Scale scores. These findings provide proof-of-concept evidence for contactless digital depression severity stratification in adolescents. Further external and prospective validation is needed to establish their utility for longitudinal monitoring and timely clinical assessment.

Objective digital measures of depression severity could complement conventional symptom assessments and facilitate more timely clinical evaluation. Here, we investigated whether task-evoked eye movements provide interpretable behavioral markers of depression severity in adolescents and young adults. In this single-center, cross-sectional study, 155 participants aged 14–20 years completed emotional-spatial free-viewing and memory-guided recall tasks and were stratified by 17-item Hamilton Depression Rating Scale scores. We examined 19 prespecified eye-movement features across six task-informed domains using covariate-adjusted ordinal-trend models. Greater depression severity was associated with lower gaze-allocation entropy (standardized $\beta=-0.599$), fewer inter-area switches ($\beta=-0.526$), longer first target-entry latency ($\beta=0.572$), and lower target dwell proportion ($\beta=-0.678$; all false-discovery-rate-adjusted $q<0.001$). Emotion-specific attentional-bias measures were not associated with severity, whereas associations involving fixation dispersion weakened after adjustment for anxiety symptoms. In a complementary machine-learning analysis incorporating 44 candidate eye-movement features and demographic covariates, a three-stage cascade classifier achieved the highest four-class stratification performance (accuracy, 0.665; macro-F1, 0.670; area under the receiver operating characteristic curve, 0.869), outperforming single-stage and two-stage approaches. Together, these findings identify altered visual exploration and memory-guided gaze control as candidate digital biomarkers of depression severity, rather than generalized negative attentional bias. These results support further prospective evaluation of task-evoked eye tracking as a complementary approach for timely severity assessment and clinical decision support.

This paper develops a three-axis Rydberg atomic receiver model for direction-of-arrival (DoA) estimation of the radio frequency (RF) signal source. Three nominally orthogonal RF local oscillators (LOs), each assigned a distinct frequency offset, project the weak incident RF field into resolvable complex beat coefficients in the optical readout. A beat-vector observation is obtained by adopting a response calibration mechanism that maps the coefficients onto the three axes of the Cartesian coordinate system. For a known circularly polarised RF signal, this observation induces a direction-dependent Rydberg steering vector, enabling maximum-likelihood DoA estimation. The pro posed procedure translates the Rydberg beat-vector front end into a highly sensitive RF field DoA sensor. Simulation results show that trace-level beat extraction supports the proposed DoA sensing workflow and provides a basis for comparison with a traditional vector antenna (VA) benchmark.

Automated radar waveform recognition classifies the waveform and modulation type of a received signal. As new waveform variants appear over time, a deployed classifier must learn them without retraining from scratch. Catastrophic Forgetting occurs when learning new waveforms overwrites prior knowledge. Continual Learning (CL) methods tackle this challenge, but prior radar studies only evaluate a few methods on narrow benchmarks. Practitioners lack evidence on which method to deploy or which mechanisms are effective. This work presents the first large-scale benchmark of continual learning for radar waveform recognition. Eighteen CL methods are evaluated on a sequential ten-task problem, drawn from four public datasets spanning 54 waveforms, under task- and class-incremental (TIL, CIL) settings and single- and multi-epoch regimes. Methods that retain data from old tasks lead both TIL and CIL settings. Plain replay alone closes 77-88% of the gap between the lower and upper practical limits under TIL, but only 46-55% under CIL. The internal mechanisms of the leading methods are ablated and statistically tested to identify effective mechanisms. Each ablation is paired against its full method over n=6 training seeds, topped up to n=12 when inconclusive, and p-values are Holm-Bonferroni corrected within each method's family. Ablation results show that several mechanisms confer smaller but statistically significant gains. Combining these mechanisms improves the leading F1 score by 3.4-5.5% and 2.7-4.9% under TIL and CIL, respectively, and the combined models exceed every benchmarked method in both settings for all SNRs. These results give researchers of radar waveform classification systems a mechanism-level basis for design.

Advection-diffusion plays an essential role in transport phenomena such as atmospheric and cloud dynamics. Improved forecasting of cloud motion, in turn, enhances solar power production forecasts and facilitates the integration of additional photovoltaic (PV) sources into the power grid. However, both classical numerical models and emerging deep learning approaches face challenges when applied to spatially irregular sensor networks such as those formed by weather stations. In this work, we adopt a graph signal processing perspective and model advection-diffusion particles as signals on a graph to capture the dynamics of these processes. We introduce a Physics-Informed Graph Neural Network (PING) for forecasting future advection-diffusion particle concentrations on irregular grids. The model incorporates a discretized advection-diffusion equation as a soft constraint in the loss function to guide learning toward physically consistent solutions. This model is on par with state of the art models on synthetic and real transport phenomena datasets, including sea surface temperature and cloud index datasets. Furthermore, the PING model is successfully coupled with models for forecasting PV power production. This hybrid approach leverages data fusion from ground-based sensors at PV stations and satellite images with cloud concentration to improve the accuracy of intra-day PV power production.

Wearable fall detection is hampered by rare fall events, scarce labeled IMU data, and the cost of training deep models on large, imbalanced corpora. Existing methods either rely on hand-crafted features with shallow classifiers, which limits learning from raw signals, or train deep networks on full datasets that require re-sampling to counter class skew and demand substantial data and compute. To address this, we propose a novel Fall detection framework via Normalized-gradient and Embedding matched Synthetic Time-frequency condensation, dubbed FALLNEST, a data-centric approach that distills large, imbalanced IMU datasets into tiny, class-balanced synthetic training sets. Building on dual-domain time-series condensation (CondTSC), FALLNEST introduces a fall-specific 7-channel input with accelerometer vector magnitude, class-balanced K-Means initialization, and normalized gradient matching with gradient clipping, combined with MMD-based embedding alignment and multi-view Fourier-domain augmentation, so that class balance arises from the condensed set itself rather than from re-sampling. We evaluate on SisFall, KFall, and MobiFall using strict subject-wise splits and naturally imbalanced test sets, reporting accuracy, Macro-F1, and PR-AUC over five seeds. A 102K-parameter TinyCNN trained on only 32 to 146 synthetic windows (about only 1% of the training windows) reaches 93.5-95.8% mean accuracy and 0.89-0.98 PR-AUC, with standard deviations of 1.0-1.7% in accuracy, demonstrating the efficacy of our approach.

For sensing-aided communications, single-endpoint sensing offers only a partial view of propagation, motivating link-centric sensing across both endpoints. We propose cross-image routing attention (CIRA) for sparse association and mutual enhancement of propagation-relevant features from dual-endpoint RGB images. Building on CIRA, CiFormer predicts millimeter-wave (mmWave) beam-pair power matrices for vehicle-to-infrastructure (V2I) communications. Experiments show that CiFormer reduces mean absolute error and improves top-1 beam-pair selection accuracy by 3.15 percentage points over the strongest baseline. Visualizations and ablation studies support dual-endpoint sensing and selective cross-view interaction in capturing propagation-relevant dependencies, suggesting broader applications in wireless tasks beyond beam prediction.

We study causal error-feedback coefficients as design variables for model quantization. Modeling instantaneous quantization errors as independent, zero-mean variables with a common fixed variance, we formulated a metric-dependent trace problem for designing feedback coefficients. The formulation resembles fixed-order GPTQ/LDLQ feedback and supports Jacobian-aware designs that account for downstream error propagation. For a positive-definite metric $\boldsymbol{H}$, we prove that the joint rotation-feedback trace minimum is $n\det(\boldsymbol{H})^{1/n}$ and present an attaining pair using the geometric mean decomposition. Experiments on synthetic networks, trained MLPs, and language models show benefits from downstream-sensitive feedback, but also reveal that trace optimality need not improve finite-level performance. Small QAT experiments further illustrate direct loss-driven feedback tuning with fixed or trainable network weights.

This paper studies the spectral compressed sensing problem, which aims to recover an $r$-spectrally-sparse signal of length $N$ from linear compressive measurements. We reformulate the problem as the recovery of a low-rank Hankel matrix. Motivated by this formulation, we propose a Lifted Restricted Isometry Property (LRIP) for the measurement matrix and show that Gaussian measurement matrices satisfy LRIP under certain conditions. Unlike classical RIP, LRIP imposes a rank constraint in the lifted matrix domain while taking measurements in the signal domain through the adjoint Hankel operator. To solve the resulting nonconvex problem efficiently, we introduce a Fast Iterative Hard Thresholding (FIHT) algorithm. Based on LRIP, we show that FIHT converges linearly to the true signal with high probability using $\mathcal{O}(r \ln r \ln^2 N)$ scaled Gaussian measurements, which is near-optimal up to log factors and improves the state-of-the-art order of $r^2$ for nonconvex Hankel methods. Extensive numerical results demonstrate the superiority of FIHT as compared to state-of-the-art convex methods in terms of phase transitions, computational time, and robustness to noise.

We study the recovery of a three-dimensional volume in single-particle cryo-electron microscopy (cryo-EM) from noisy tomographic projections acquired at unknown viewing rotations, drawn independently and uniformly from $\mathrm{SO}(3)$. We show that, in a spherical-shell representation satisfying an explicit shell-separation condition, the first three moments generically determine the underlying real volume up to its intrinsic global $\mathrm{O}(3)$ ambiguity. The proof is constructive and yields a frequency-marching procedure that successively recovers the volume. Consequently, consistent recovery in the high-noise regime has sample complexity $n=\omega(\sigma^6)$, where $n$ is the number of observations and $\sigma^2$ is the noise variance. Numerical experiments validate the constructive recovery scheme under finite sampling and additive noise, and indicate that direct third-moment optimization can succeed beyond the structural assumptions required by the constructive proof.

Direct Link Interference (DLI) remains a major bottleneck in Orthogonal Frequency Division Multiplexing (OFDM)-based Ambient Backscatter Communication (AmBC) systems. This letter proposes Shuf-Scatter, a scheme that applies a deterministic permutation to the reflected OFDM waveform to create a distinct correlation-domain signature. By exploiting the quasi-orthogonality between the reshuffled backscatter signal and the direct-link component, the proposed receiver effectively mitigates DLI and enables reliable detection without requiring dedicated spectral resources, or instantaneous Backscatter-Link (BL) Channel State Information (CSI). Analytical and simulation results demonstrate that Shuf-Scatter achieves superior Bit Error Rate (BER) performance under strong DLI while preserving the integrity of the legacy OFDM transmission.

Electrocardiogram (ECG) models need supervision that targets evolving weaknesses and remains faithful to waveform evidence. We introduce ECG-RSI, a recursive self-improvement framework combining verified waveform synthesis with feedback-guided intervention search. Signal remeasurement verifies waveform edits; construction errors guide generator repair, while learner errors motivate interventions compared on a separate selection set. Across seven benchmarks, three rounds raise Qwen3.5-35B-A3B's mean question-answering accuracy from 34.57% to 56.04% and mean classification F1 from 24.27 to 34.03. Across the retained trajectory, the preferred supervision shifts from direct reinforcement to confusion-focused contrasts. Continued feedback correction yields diminishing external gains. These results motivate separating error diagnosis from intervention selection and checking whether gains on the feedback set transfer to independent benchmarks.

We propose FedLoRD, a personalized federated learning (PFL) method based on the low-rank structure of the differences between the global and personalized model parameters. PFL is a branch of federated learning that aims to learn models tailored to the local data of each client under biased local data distributions. In its local training, each client learns a personalized model by regularizing the difference between its model parameters and the global ones sent from the server. The regularization function needs to be designed with priors on the model parameter difference; however, the existing PFL methods rely on empirical choices (e.g., $\ell_1$- and $\ell_2$-norms), which do not take into account the actual characteristics of the difference. The resulting mismatch of priors can lead to suboptimal personalized models. To address this issue, we propose a PFL method whose regularizer is built on an observed characteristic of the model parameter differences: they are approximately low-rank at every layer. In our method, we first formulate a local training problem with nuclear norm regularization on the layer-wise differences. We further rewrite this problem via the factorized representation of the nuclear norm so that it can be efficiently solved by a plain stochastic gradient descent method. Image classification experiments on three benchmark datasets demonstrate that FedLoRD outperforms several existing PFL methods.

A passive extremely large-scale reconfigurable intelligent surface (XL-RIS) offers a position-sensitive aperture for near-field sensing, but its element-wise signals are not directly observed. Recovering this virtual RIS observation (VOR) from fewer base-station measurements requires prior information. This paper investigates when calibrated propagation geometry or paired training data provides the more effective reconstruction prior. For a common sensing operator and target signal, we develop off-grid near-field sparse parametric reconstruction (OG-NF-SPR) and compare it with compact and DenseNet-based deep reconstructors. OG-NF-SPR uses reflected-path projection for position initialization, followed by Bayesian estimation of all path gains and continuous position refinement. A causal extension incorporates temporal correlation without trajectory labels. Controlled simulations use near-field UAV tracking as an illustration and separately examine noise, position-distribution shift, calibration error, and propagation mismatch. Under matched propagation at 20 dB, OG-NF-SPR achieves approximately 67% lower reconstruction NMSE than the two nominal deep networks. At low SNR, statistical averaging is more effective than selecting a single geometric explanation. Under sufficiently strong repeatable mismatch, a same-size network trained on the mismatch family outperforms the nominal physical estimator, whereas nominal training does not provide this benefit. Temporal regularization also reduces position RMSE from 0.653 m to 0.225 m on a shared trajectory. The results relate reconstruction performance to measurement quality, calibration accuracy, and training coverage, providing guidance on when geometry can replace supervised labels and when additional data are useful.

The shift towards 6G wireless networks calls for advanced waveforms to support emerging applications such as ultra-reliable and massive machine-type communications. Among the proposed candidates, OTFS, ODDM, OTSM, and OCDM share the common feature of exploiting the delay domain for data transmission. While these waveforms offer significant advantages in high-mobility and sensing scenarios, their integration into multi-user uplink systems reveals a critical challenge: timing misalignment (TM). Unlike OFDM, where residual timing offsets translate into phase rotations, delay-based waveforms experience actual shifts of resources, whose impact we investigate in this work. We show that the aforementioned waveforms can be unified under a time-invariant setup through a common input–output relation, which enables a unified analysis of TM in uplink resource allocation. The results reveal a fundamental trade-off between interference mitigation and guard overhead in the presence of TM. We further benchmark the observed behavior against OFDM under Doppler spread as a reference impairment and highlight the analogy between TM in delay-based allocation and Doppler spread in frequency-based allocation. The results show that delay-based waveforms can maintain a net performance advantage over OFDM in the considered setup. Beyond this unified Doppler-free analysis, we evaluate the joint impact of Doppler spread and TM on each waveform individually. We also investigate BER performance and show how coding can mitigate the resulting interference. This study therefore establishes a problem framework that motivates the development of future compensation and scheduling strategies for practical multi-user operation of delay-based waveforms.

With the incessant demand for green and sustainable communications, energy efficiency (EE) has become a cornerstone of sixth-generation (6G) network design, especially for cell-free millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems, which can achieve significant throughput gains by leveraging large-scale antenna arrays. Adaptive radio frequency (RF) chain activation (ARFA) is a key technique for reducing power consumption without severely degrading the achievable rate, thereby enhancing the total EE of 6G MIMO systems. In this paper, we propose a minimum-cost maximum-flow (MCMF)-based ARFA scheme to reduce unnecessary power consumption and maximize the total EE of uplink mmWave cell-free massive MIMO systems. Simulation results indicate that the proposed approach can significantly improve the total EE while maintaining a relatively high achievable rate and acceptable computational complexity.

We develop a Fisher-information framework for optimizing single-photon detector gating in periodic experiments, in contrast to previous more empirical or heuristic gating choices. After each photon detection, such a detector is insensitive for a dead time, so that an early background detection can obscure a later signal. Gating preserves availability for informative photons by selectively deactivating the detector. In our high-pile-up simulation of distance-attenuated lidar, our practical data-independent gating design reduces the worst-case asymptotic range variance by more than half relative to the free-running acquisition (which does not use any gating). More generally, we characterize achievable detector-active frequencies as a convex polytope and construct an adaptive rule that asymptotically attains an optimal "oracle" design without prior knowledge of the physical parameters. We also derive practical criteria that quantify the potential gains of gating and/or photon attenuation over unattenuated free-running acquisition.

Deep unrolling methods for inverse problems augment traditional reconstruction algorithms with learned priors to improve their performance. These methods have been developed under the implicit assumption that the geometry of the measurement operator does not change between training and deployment; when this is no longer the case, the quality of obtained reconstructions can degrade significantly. In this paper, we argue that this is not an inherent limitation of the deep unrolling paradigm and can be mitigated by a careful choice of the underlying iterative algorithm. We propose a deep unrolling framework for the phase retrieval problem that augments the alternating projections method with a learned denoising step. Our method obtains high-quality reconstructions without the need for retraining in settings where conventional model-based alternatives struggle, such as when the dynamic range of the measurement operator changes significantly at test time.

Many optimization problems that arise in signal processing applications are based around optimizing vectors/matrices of numeric sequences. These problems are often inherently non-convex due to non-linear constraints. Such problems are frequently solved through convex relaxations of the original problem to allow for computationally simple algorithms to be leveraged, albeit with the potential for appreciable performance costs in the solution space. When the constraints of a problem can be implicitly satisfied through the structure of a Riemannian manifold, optimizing directly on said manifold affords the opportunity to solve the problem in an unconstrained fashion. Therefore, when feasible, recasting the constrained optimization problem in Euclidean space as an unconstrained optimization over a Riemannian manifold offers greater flexibility and improved efficiency over convex relaxation based approaches. This paper introduces the minimal smooth optimization theory required and adapts the notation to a radar signal design context. In addition, we apply our approach to two different problems in direct parallel to contemporary convex relaxation-based signal design literature.

Standard diffusion processes with isotropic noise corruption are not spectrally neutral. They inherit a coarse-to-fine generation hierarchy from the nonuniform data spectrum, because each frequency band loses signal-to-noise ratio (SNR) at its own rate. For signals on a graph, the relevant spectrum is that of the graph shift operator (GSO). A natural construction, therefore, builds both the diffusion dynamics and the denoiser from graph filters of the GSO, and any such design also takes a position on this hierarchy. We expose this hierarchy as a tunable axis through a family of colored score-based diffusions. Fixing the terminal prior to a Gaussian colored by the GSO couples the drift and diffusion through a positive spectral rate, which acts as a clock for each graph-frequency band. The clock sets when a band loses and recovers information, and the forward processes of standard and certain graph-aware diffusions appear as members of this family. A GSO-valued EDM preconditioning splits the denoiser into an analytic Wiener head that carries the Gaussian second-order structure and a graph neural network (GNN) that learns the remaining non-Gaussian dependencies. Numerical experiments show that colored diffusions reach a given generation quality in far fewer sampling steps than standard diffusion.

This letter studies the throughput and reliability of power-constrained rate-splitting multiple access (RSMA) with variable-length incremental-redundancy hybrid automatic repeat request (IR-HARQ) under finite blocklength (FBL) transmission and imperfect channel state information at the transmitter (CSIT). While perfect-CSIT feedback-delay tolerance approaches a natural ceiling as signal-to-noise ratio (SNR) grows, we show imperfect CSIT imposes a strictly lower, finite ceiling set solely by CSIT quality, one that transmit power cannot overcome. We derive analytical expressions and tight approximations for common- and private-stream decoding failure probabilities, capturing FBL and CSIT uncertainty via a unified CSIT-degradation factor. Also, we show that residual interference saturates effective stream power at high SNR, limiting reliability and producing this delay-tolerance ceiling. Simulations validate the analysis and quantify the ceiling, offering design insights for delay-robust RSMA under imperfect CSIT.

Decoding visual stimuli from the spiking activity of retinal ganglion cells (RGCs) informs both our understanding of the retinal code and the design of visual prostheses. Natural vision is dynamic, yet state-of-the-art decoders mostly reconstruct single frames with artificial neural networks that process dense, continuous-valued signals rather than the retina's sparse, asynchronous spikes. We asked whether dynamic scenes can be reconstructed and tracked entirely within the spike domain, and at what computational cost. We built an end-to-end spiking neural network (SNN) whose neurons integrate the recorded RGC spikes over time. Populations of up to 1836 RGCs were recorded ex vivo from mouse retina on high-density microelectrode arrays while flashed images, moving bars, drifting gratings and randomly moving handwritten digits were presented. For moving digits, one shared spiking encoder fed two readouts, one reconstructing the image and one decoding the digit's position. Compute energy was estimated from counted synaptic operations. The SNN decoded what and where at once. It reconstructed the stimuli with a structural similarity (SSIM) of up to 0.93, refining the reconstruction as spikes accumulated, and tracked a randomly moving target with a mean error of 1.33 pixels on a $32\times32$ grid. Its fidelity nearly matched that of the best of 17 larger spiking and artificial decoders (5.9 million to 1.7 billion parameters, versus 5.4 million), and at a mean firing rate of 14.2% it needed an estimated $\sim$70-fold less compute energy than a dense artificial network of the same architecture. Dynamic visual scenes can thus be decoded from retinal population activity in the spike domain, with a compact network and at a compute cost compatible with implantable and wearable neural interfaces, supporting spike-native decoders for visual prostheses.

Solar-blind ultraviolet communication (UVC) exploits near-zero solar background radiation and strong atmospheric scattering in the 200-280 nm band, enabling photon-level daytime communication and propagation modes beyond conventional line-of-sight (LOS) links. However, existing studies mainly focus on individual channel types, and a unified channel classification is still lacking. This paper presents a taxonomy of all four fundamental solar-blind UVC channels together with representative experimental validations and a comparative performance analysis. The proposed framework provides practical guidelines for selecting appropriate channel architectures for future solar-blind UVC systems.

This paper investigates a shift-sum decoding algorithm that employs dual codewords. Existing dual codeword shift-sum decoding schemes mainly rely on minimum-weight dual codewords. However, the number of such codewords is often limited, which restricts the amount of reliability information available to the decoder. To address this issue, we extend the component counting mechanism from minimum-weight dual codewords to dual codewords whose Hamming weights lie within a prescribed upper bound. We further introduce a normalized component counting rule to suppress the overcounting bias caused by the much larger populations of higher-weight classes. Based on this framework, we develop a hard-decision iterative error reduction decoder and a soft information-aided iterative error reduction decoder. Numerical results show that the proposed method consistently outperforms existing minimum-weight shift-sum baselines especially for short codes where additional dual codeword information is particularly valuable.

Solar-blind ultraviolet (UV) optical wireless communication (OWC) enables non-line-of-sight (NLOS) transmission through atmospheric scattering. This work presents a comprehensive experimental and simulation study of a 100 m outdoor UVC link with comparative measurements under LOS-blocked and open-field conditions. The results show that LOS-blocked scattering-mediated links exhibit substantially higher path loss and narrower bandwidth than corresponding open-field measurements, highlighting the impact of residual LOS components in practical diffused beam UVC experiments. A photon-tracing Monte Carlo (MC) framework incorporating realistic transmitter/receiver characteristics and obstacle geometry is further developed to reproduce the measured channel characteristics with high accuracy. Despite the severe path loss and pulse broadening effects, reliable 20-200 kbps transmission is experimentally demonstrated over the 100 m LOS-blocked link using on-off keying (OOK) and pulse-position modulation (PPM), representing the longest-distance and highest-data-rate experimental demonstration of an explicitly LOS-blocked scattering-mediated UVC link reported to date.

Tensor decoupling algorithms recover compact representations of multivariate functions by combining linear factor matrices with univariate internal nonlinearities. We identify two limitations of recent methods. First, their performance is not invariant to mismatched output or gradient magnitudes, and second, their spline internals require manual tuning of their degrees of freedom. We propose two lightweight fixes. The first is a preprocessing step that rescales the input Jacobian tensor, removing both the bias towards larger magnitude outputs and the ill-conditioning of certain factor updates caused by such a mismatch. The second replaces B-spline internals by penalized splines coupled with generalized cross-validation, making the method less dependent on the user selecting the right number of control points. Finally, we release an efficient open-source JAX implementation of our algorithm.

Hybrid analog–digital arrays reduce hardware cost in integrated sensing and communications (ISAC), but spatial compression and beam-dependent scan responses complicate multi-target estimation with few radio-frequency (RF) chains. To address these challenges, this paper proposes a burst beam-scanning multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) framework that reuses downlink reference signals for joint range, radial-velocity, elevation, and azimuth estimation. Specifically, a structured global–local dual-tensor estimator separates targets by using scanning observations and refines their parameters from selected-beam RF-chain responses, with delay–Doppler signatures maintaining cross-stage target correspondence. The recovered subarray responses yield closed-form elevation and azimuth estimates through exact sum–difference relations, avoiding small-angle approximations and fine angular grid searches. To further improve Doppler accuracy, spatial-mask-aided weighted least squares exploits the longer inter-burst temporal aperture while resolving phase ambiguity using intra-burst estimates. The joint multi-target Cramér–Rao lower bound (CRLB) and corresponding three-dimensional (3D) positioning bound are also derived to characterize estimation performance. Simulation results show that the proposed scheme achieves near-CRLB estimation accuracy at high transmit powers and centimeter- to millimeter-level 3D positioning with only four RF chains, while maintaining low computational cost.

Existing deep-learning-based channel state information (CSI) feedback methods reduce feedback overhead by compressing high-dimensional CSI into low-dimensional representations and/or exploiting historical CSI to predict future CSI. However, these methods may become unreliable when user equipment (UE) suddenly changes its motion. To address this issue, we propose a prediction-aided coding (PAC) framework for generative CSI feedback, which adaptively encodes CSI based on predicted channel/CSI distributions and the observed CSI in real time. Each CSI frame is represented by multiple CSI tokens obtained without using historical CSI, making token generation robust to abrupt UE motion. In the first stage, the conditional distribution, instead of a deterministic value, of the next-frame CSI tokens is predicted based on historical tokens. In the second stage, once the next frame arrives, the predicted distribution is used for variable-length entropy coding of its CSI tokens. To further reduce feedback overhead and inference latency, we propose efficient designs for both stages. For the first stage, a customized causal Transformer with a new positional embedding strategy accurately predicts next-frame token probabilities. For the second stage, a parallel causal decoding strategy reduces the inference latency caused by sequential token decoding. Extensive experiments show that, compared with existing history-aware baselines, PAC reduces feedback overhead by approximately 72% to 84% while achieving higher reconstruction accuracy. Additionally, PAC maintains CSI reconstruction accuracy under random-walk trajectories, whereas existing history-aware baselines suffer sharp degradation in reconstruction accuracy.

This paper considers a distributed estimation system where edge nodes transmit averaged features to a center node for parameter estimation. A key challenge arises when the center node possesses auxiliary side information: how to design local extractors that are universally optimal for any downstream task, while avoiding redundancy with the side information. To tackle this problem, we orthogonally decompose the joint function space and demonstrate in the weak-dependence regime that the modal decomposition of the canonical dependence kernel's conditional-additive component yields optimal, task-independent features. We construct a distributed nested H-score to learn this decomposition and show that, under a limited feature budget, the mean-squared error (MSE) gap to the best task-specific features is determined solely by the task's alignment with the discarded, least informative modes, and vanishes to leading order for every square-integrable task once the budget is sufficient.

The introduction of Reconfigurable Intelligent Surfaces (RIS) technology marks a transformative era in modern communication systems, enhancing coverage and facilitating higher data transmission rates. While recent research has delved into the integration of RIS into satellite networks, a critical and practical gap persists concerning its implementation in the satellite-Very Small Aperture Terminal (VSAT) downlink, specifically amplifying contributions from the VSAT side. This paper addresses this gap by proposing a simple and engineering methodology to optimize RIS position and orientation, incorporating considerations for the antennas' radiation patterns and the system's topology to streamline optimization variables. The primary objective of this paper is to alleviate the double fading constraint in the reflect link without the need for active elements, achieved by leveraging the Fresnel zone of the RIS. The improvement power ratio depends on the frequency and the VSAT 's diameter. The results show that for a VSAT with diameter 1m the gain is up to 9dB at 2 GHz (S band) and up to 14dB at 4 GHz (C band). Additionally, the RIS channel equation is refined, encompassing the tropospheric attenuation and accounting for electromagnetic wave polarization.